Object detection aims to classify and localize objects, which receives remarkable progress in recent years [1]}, [2]}, [3]}.
However, the strong performance heavily relies on a large number of labeled training samples, which requires extensive labeling for each object and is expensive to acquire.
On the contrary, humans can recognize novel classes with the aid of only a few annotated samples,
which is a desirable ability object detectors should have. Thus great interests have been invoked to explore Few-Shot Object Detection (FSOD), which aims to train an object detector for novel classes with the help of abundant data on base classes and few shot samples on novel classes.
